1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
8/8/2022 Comida 17890 Tami NA
8/8/2022 Comida 41390 Tami NA
19/8/2022 VTR 21990 Andrés NA
18/8/2022 Comida 21860 Andrés NA
19/8/2022 Comida 5213 Andrés NA
22/8/2022 Parafina 23300 Tami NA
24/8/2022 Comida 57780 Tami NA
26/8/2022 mantencion toyotomi 34000 Andrés mantencion toyotomi
27/8/2022 Comida 19410 Tami NA
29/8/2022 Netflix 8320 Tami NA
31/8/2022 Incoludido 21000 Tami NA
31/8/2022 Electricidad 89272 Andrés PAC ENEL 01686518
31/8/2022 Enceres 12000 Andrés Visita gasfiter
3/9/2022 Comida 59225 Andrés Lider
3/9/2022 Comida 21350 Andrés Laflordeloto.cl
5/9/2022 Comida 78035 Tami NA
7/9/2022 Enceres 15400 Andrés era del mes pasado pero igual
10/9/2022 Agua 10860 Andrés PAC AGUAS ANDIN 000000005687837
10/9/2022 basureros 20000 Andrés basureros
10/9/2022 Comida 41870 Andrés NA
12/9/2022 Comida 3020 Andrés Reste el costa rama
12/9/2022 Comida 20563 Andrés Laflordeloto.cl
12/9/2022 Enceres 57000 Andrés Arreglo wc+ visita
15/9/2022 Comida 38863 Tami NA
16/9/2022 Diosi 12136 Tami Pipeta antipulgas
19/9/2022 Comida 5070 Andrés NA
20/9/2022 Comida 62208 Tami NA
21/9/2022 VTR 17990 Andrés Entel con mercadopago
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 4.3197e+08   2    4.7807 0.0088 ** 
## lag_depvar    7.6502e+10   1 1693.3311 <2e-16 ***
## Residuals     2.2228e+10 492                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff        lwr      upr     p adj
## 1-0  7228.838   907.0548 13550.62 0.0202764
## 2-0 27581.384 21750.6611 33412.11 0.0000000
## 2-1 20352.546 16820.1610 23884.93 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
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## 26   17692.29             0   24642.71
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## 231  80355.00             2   86724.86
## 232  74875.14             2   80355.00
## 233  81347.00             2   74875.14
## 234  66062.43             2   81347.00
## 235  56946.43             2   66062.43
## 236  47732.14             2   56946.43
## 237  38129.71             2   47732.14
## 238  42928.29             2   38129.71
## 239  45392.57             2   42928.29
## 240  37895.43             2   45392.57
## 241  30660.29             2   37895.43
## 242  42430.86             2   30660.29
## 243  35845.14             2   42430.86
## 244  40350.43             2   35845.14
## 245  31494.71             2   40350.43
## 246  30013.29             2   31494.71
## 247  34197.57             2   30013.29
## 248  37430.14             2   34197.57
## 249  26932.43             2   37430.14
## 250  33729.86             2   26932.43
## 251  38081.43             2   33729.86
## 252  44028.00             2   38081.43
## 253  47139.71             2   44028.00
## 254  46558.86             2   47139.71
## 255  58350.57             2   46558.86
## 256  78380.00             2   58350.57
## 257  78168.29             2   78380.00
## 258  70510.86             2   78168.29
## 259  72207.14             2   70510.86
## 260  67881.00             2   72207.14
## 261  69536.43             2   67881.00
## 262  62390.71             2   69536.43
## 263  50113.14             2   62390.71
## 264  45565.57             2   50113.14
## 265  45805.29             2   45565.57
## 266  41348.57             2   45805.29
## 267  51426.86             2   41348.57
## 268  47160.57             2   51426.86
## 269  51907.43             2   47160.57
## 270  49751.43             2   51907.43
## 271  54407.43             2   49751.43
## 272  54746.29             2   54407.43
## 273  61634.57             2   54746.29
## 274  58926.43             2   61634.57
## 275  69999.29             2   58926.43
## 276  63044.86             2   69999.29
## 277  63285.29             2   63044.86
## 278  61395.43             2   63285.29
## 279  67969.43             2   61395.43
## 280  60792.57             2   67969.43
## 281  56859.14             2   60792.57
## 282  44899.43             2   56859.14
## 283  43064.14             2   44899.43
## 284  62790.29             2   43064.14
## 285  69120.71             2   62790.29
## 286  69589.43             2   69120.71
## 287  66633.29             2   69589.43
## 288  65588.57             2   66633.29
## 289  70168.57             2   65588.57
## 290  74644.71             2   70168.57
## 291  52891.00             2   74644.71
## 292  41560.57             2   52891.00
## 293  34704.86             2   41560.57
## 294  46520.00             2   34704.86
## 295  50231.00             2   46520.00
## 296  49216.71             2   50231.00
## 297  76914.86             2   49216.71
## 298  83720.71             2   76914.86
## 299  84485.00             2   83720.71
## 300  89765.00             2   84485.00
## 301  87702.86             2   89765.00
## 302  82013.86             2   87702.86
## 303  85982.43             2   82013.86
## 304  57248.43             2   85982.43
## 305  52968.43             2   57248.43
## 306  52601.86             2   52968.43
## 307  45493.29             2   52601.86
## 308  42298.86             2   45493.29
## 309  46423.71             2   42298.86
## 310  37898.00             2   46423.71
## 311  36435.14             2   37898.00
## 312  30209.57             2   36435.14
## 313  34541.86             2   30209.57
## 314  33604.71             2   34541.86
## 315  37990.71             2   33604.71
## 316  35683.43             2   37990.71
## 317  65201.86             2   35683.43
## 318  62730.57             2   65201.86
## 319  64589.14             2   62730.57
## 320  73744.86             2   64589.14
## 321  76477.71             2   73744.86
## 322 105647.43             2   76477.71
## 323 103790.29             2  105647.43
## 324  76122.29             2  103790.29
## 325  74746.14             2   76122.29
## 326  72865.71             2   74746.14
## 327  63652.57             2   72865.71
## 328  60358.29             2   63652.57
## 329  25957.14             2   60358.29
## 330  30178.43             2   25957.14
## 331  30681.57             2   30178.43
## 332  33337.29             2   30681.57
## 333  32582.71             2   33337.29
## 334  39184.43             2   32582.71
## 335  40415.71             2   39184.43
## 336  34975.43             2   40415.71
## 337  34076.14             2   34975.43
## 338  34221.14             2   34076.14
## 339  28862.57             2   34221.14
## 340  35729.86             2   28862.57
## 341  36489.29             2   35729.86
## 342  36785.14             2   36489.29
## 343  37787.71             2   36785.14
## 344  39832.14             2   37787.71
## 345  41917.86             2   39832.14
## 346  41633.57             2   41917.86
## 347  33557.00             2   41633.57
## 348  22759.57             2   33557.00
## 349  28877.86             2   22759.57
## 350  27574.00             2   28877.86
## 351  27104.71             2   27574.00
## 352  24376.14             2   27104.71
## 353  29732.29             2   24376.14
## 354  34030.00             2   29732.29
## 355  39139.71             2   34030.00
## 356  37066.57             2   39139.71
## 357  38509.29             2   37066.57
## 358  40957.29             2   38509.29
## 359  49423.00             2   40957.29
## 360  50053.29             2   49423.00
## 361  50284.14             2   50053.29
## 362  53103.86             2   50284.14
## 363  50223.00             2   53103.86
## 364  49587.14             2   50223.00
## 365  41167.71             2   49587.14
## 366  37958.71             2   41167.71
## 367  33582.29             2   37958.71
## 368  31039.43             2   33582.29
## 369  26526.57             2   31039.43
## 370  34869.43             2   26526.57
## 371  37487.43             2   34869.43
## 372  46514.43             2   37487.43
## 373  39613.43             2   46514.43
## 374  38980.57             2   39613.43
## 375  37306.14             2   38980.57
## 376  36771.29             2   37306.14
## 377  26317.00             2   36771.29
## 378  31580.71             2   26317.00
## 379  23626.57             2   31580.71
## 380  33035.71             2   23626.57
## 381  44864.57             2   33035.71
## 382  48946.14             2   44864.57
## 383  46969.57             2   48946.14
## 384  49249.57             2   46969.57
## 385  56370.14             2   49249.57
## 386  67228.71             2   56370.14
## 387  59457.29             2   67228.71
## 388  53124.71             2   59457.29
## 389  52814.14             2   53124.71
## 390  61262.00             2   52814.14
## 391  61861.14             2   61262.00
## 392  71784.71             2   61861.14
## 393  59313.29             2   71784.71
## 394  61107.00             2   59313.29
## 395  60603.43             2   61107.00
## 396  60012.57             2   60603.43
## 397  58280.43             2   60012.57
## 398  56862.71             2   58280.43
## 399  41704.43             2   56862.71
## 400  51533.00             2   41704.43
## 401  50388.71             2   51533.00
## 402  49205.29             2   50388.71
## 403  56533.29             2   49205.29
## 404  47996.14             2   56533.29
## 405  47207.57             2   47996.14
## 406  45292.00             2   47207.57
## 407  40343.43             2   45292.00
## 408  39004.86             2   40343.43
## 409  36788.43             2   39004.86
## 410  30027.57             2   36788.43
## 411  39040.14             2   30027.57
## 412  42390.14             2   39040.14
## 413  36291.14             2   42390.14
## 414  30668.29             2   36291.14
## 415  47693.00             2   30668.29
## 416  52094.43             2   47693.00
## 417  56592.57             2   52094.43
## 418  47971.43             2   56592.57
## 419  43762.43             2   47971.43
## 420  42246.71             2   43762.43
## 421  46352.43             2   42246.71
## 422  33094.86             2   46352.43
## 423  32784.86             2   33094.86
## 424  26212.43             2   32784.86
## 425  32611.57             2   26212.43
## 426  42144.86             2   32611.57
## 427  50034.86             2   42144.86
## 428  46332.00             2   50034.86
## 429  42976.29             2   46332.00
## 430  39456.29             2   42976.29
## 431  39328.29             2   39456.29
## 432  35296.14             2   39328.29
## 433  30875.43             2   35296.14
## 434  27709.00             2   30875.43
## 435  29513.29             2   27709.00
## 436  31630.43             2   29513.29
## 437  29346.14             2   31630.43
## 438  34916.86             2   29346.14
## 439  42020.86             2   34916.86
## 440  38303.00             2   42020.86
## 441  37966.43             2   38303.00
## 442  41408.14             2   37966.43
## 443  38988.14             2   41408.14
## 444  43555.29             2   38988.14
## 445  38114.00             2   43555.29
## 446  27847.86             2   38114.00
## 447  26517.00             2   27847.86
## 448  39518.29             2   26517.00
## 449  39153.71             2   39518.29
## 450  45623.14             2   39153.71
## 451  40627.43             2   45623.14
## 452  41027.71             2   40627.43
## 453  42882.86             2   41027.71
## 454  47139.43             2   42882.86
## 455  35547.57             2   47139.43
## 456  41099.00             2   35547.57
## 457  35859.57             2   41099.00
## 458  44524.57             2   35859.57
## 459  48554.29             2   44524.57
## 460  51554.29             2   48554.29
## 461  47810.29             2   51554.29
## 462  50490.00             2   47810.29
## 463  50720.71             2   50490.00
## 464  52720.71             2   50720.71
## 465  52145.57             2   52720.71
## 466  55515.57             2   52145.57
## 467  52457.00             2   55515.57
## 468  58239.57             2   52457.00
## 469  50523.57             2   58239.57
## 470  47788.57             2   50523.57
## 471  46170.00             2   47788.57
## 472  42305.57             2   46170.00
## 473  46605.57             2   42305.57
## 474  55149.57             2   46605.57
## 475  48769.57             2   55149.57
## 476  50719.43             2   48769.57
## 477  44753.71             2   50719.43
## 478  42898.00             2   44753.71
## 479  46141.14             2   42898.00
## 480  34022.57             2   46141.14
## 481  26651.86             2   34022.57
## 482  28791.86             2   26651.86
## 483  31879.00             2   28791.86
## 484  33584.71             2   31879.00
## 485  34690.43             2   33584.71
## 486  27410.43             2   34690.43
## 487  41755.00             2   27410.43
## 488  49379.57             2   41755.00
## 489  57198.86             2   49379.57
## 490  51144.57             2   57198.86
## 491  56677.43             2   51144.57
## 492  65416.43             2   56677.43
## 493  69779.71             2   65416.43
## 494  54046.00             2   69779.71
## 495  43259.57             2   54046.00
## 496  40998.57             2   43259.57
## 497  41368.57             2   40998.57
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   340 49815.65 16046.644
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##             2             3             4             5             6 
##   2059.953113   4057.969548   -555.505010   2422.958594  -3004.104781 
##             7             8             9            10            11 
##    506.216054  -5674.271155  -1166.897237  -3943.045234   -372.919776 
##            12            13            14            15            16 
##  -4901.082722  -1543.740429   -834.489515    437.275838  -3197.054057 
##            17            18            19            20            21 
##   -318.179005  -2078.900511   6660.532553  -1531.473814  -1203.025014 
##            22            23            24            25            26 
##   1485.167788  -1192.687399    234.130269   1689.104901  -7123.175724 
##            27            28            29            30            31 
##    976.721515   8207.417845    368.602224    -63.977757  -2447.789783 
##            32            33            34            35            36 
##   1548.643495   4533.415054   1056.140910   2317.857933  -1953.167425 
##            37            38            39            40            41 
##   4543.378713   4265.686698  -2340.023871  -3021.491947  -1123.913442 
##            42            43            44            45            46 
## -10745.766335   7362.821539   2568.491952   1357.885636   8087.159441 
##            47            48            49            50            51 
##    611.604286   6458.373135   6605.467278  -6026.276760  -4879.026929 
##            52            53            54            55            56 
##  -5098.425316  -7926.129179   6188.904758  -4069.548686  -4859.027865 
##            57            58            59            60            61 
##   3921.677797    917.217755    -13.176564    158.684140  -4983.397356 
##            62            63            64            65            66 
##  18173.939624   3550.658809  -3751.322201   5859.508846   7243.486845 
##            67            68            69            70            71 
##  14497.798493   1464.059554 -13425.326088  -1396.603106   4573.726049 
##            72            73            74            75            76 
##  -4995.017534  -4452.083603 -10507.313769   2534.143478  -5358.896042 
##            77            78            79            80            81 
##   1138.470227  -6808.839212    646.717285  -2271.493241  -2604.358098 
##            82            83            84            85            86 
##  -3834.285304   -423.876408   2417.638135   3835.660753    513.046261 
##            87            88            89            90            91 
##   -456.774041    224.052188   4324.151180  -1175.157997   1148.359012 
##            92            93            94            95            96 
##  -2075.313038  -1039.087148    189.412500    283.527959  -7478.695617 
##            97            98            99           100           101 
##   2450.573405  -8568.703765  -2848.608710  -3937.751958  -1618.468130 
##           102           103           104           105           106 
##  -1145.376540   3291.407333  -2268.875168   2674.791393  -1106.957319 
##           107           108           109           110           111 
##   1023.594244   2626.192554  -3139.336214  -4687.203190   -784.781073 
##           112           113           114           115           116 
##   1966.712086  11734.663086  -1292.067555   2634.067857   4213.060599 
##           117           118           119           120           121 
##   3427.935569  -1190.949800  -4788.052489  -3752.638003   2321.744836 
##           122           123           124           125           126 
##  -1748.006298   1339.415513   8847.403511    772.956468     59.285014 
##           127           128           129           130           131 
##  -2584.609793   2617.852056   7000.468793    915.423198  -8591.769288 
##           132           133           134           135           136 
##   1730.093634   4105.841394  -3220.223597  -1446.126543   -866.951944 
##           137           138           139           140           141 
##  -3885.367238   1206.371367   -483.940483  -2900.158012   1750.879069 
##           142           143           144           145           146 
##  -1865.251156  -7802.033554   2120.027721  -3424.428616   2175.577436 
##           147           148           149           150           151 
##   -209.007032   1066.929876   -328.714198   1381.214576   1201.822947 
##           152           153           154           155           156 
##   3360.891906  -4882.772154  -1157.698862  -3212.888853   6000.024808 
##           157           158           159           160           161 
##   9740.810373  -3090.512457  -4422.815793   3981.762152    530.738559 
##           162           163           164           165           166 
##   3020.342545  -5615.225377  -6414.158259   4529.442610  17719.117451 
##           167           168           169           170           171 
##   3809.029205   -237.550631  -2271.723550   -902.552011   3805.682035 
##           172           173           174           175           176 
##    -37.276673  -7876.410825   3129.895786   4566.398732    833.020442 
##           177           178           179           180           181 
##   8957.089903  -9105.328093  -3245.238983 -10489.814859 -10904.590923 
##           182           183           184           185           186 
##   1644.813744   9672.102872  -1140.953644   6221.994395   6794.478743 
##           187           188           189           190           191 
##  13343.677585   8515.408029  -4030.729342   2550.222960  10448.697668 
##           192           193           194           195           196 
##  -1631.687837  -2396.104328 -10193.290549  -6178.511368   1473.105460 
##           197           198           199           200           201 
##  -5006.292887  -9527.693852   5725.399756  -2783.723322  -1410.310725 
##           202           203           204           205           206 
##   -497.593758   6797.236648  10120.524781    730.886478   3078.893801 
##           207           208           209           210           211 
##   3234.688974   5904.518193  12917.319777  -5693.587834 -11228.627731 
##           212           213           214           215           216 
##  -5487.576444 -10356.756338  -4758.267726   1874.448854 -12690.121852 
##           217           218           219           220           221 
##  16805.484346   8066.347546   1715.057542  26865.546208  12484.138706 
##           222           223           224           225           226 
##   7217.850071  13887.403264  -4127.913268  -1871.179823   3703.407694 
##           227           228           229           230           231 
##    290.211204   2709.177515   8977.461758   5761.446172  -1984.409257 
##           232           233           234           235           236 
##  -1851.900173   9448.160250 -11538.647765  -7187.687589  -8370.030141 
##           237           238           239           240           241 
##  -9853.917781   3405.180249   1641.531237  -8026.849312  -8656.394834 
##           242           243           244           245           246 
##   9488.930456  -7467.621993   2840.217782  -9985.022478  -3663.840936 
##           247           248           249           250           251 
##   1825.704770   1371.577534 -11974.297127   4072.478140   2434.957869 
##           252           253           254           255           256 
##   4547.438207   2419.735674   -902.796796  11400.700241  21040.662059 
##           257           258           259           260           261 
##   3181.379558  -4289.511375   4153.596525  -1667.113076   3800.001930 
##           262           263           264           265           266 
##  -4804.280609 -10785.892078  -4515.916788   -269.419348  -4937.341567 
##           267           268           269           270           271 
##   9067.674712  -4078.406748   4427.397780  -1910.972422   4644.640287 
##           272           273           274           275           276 
##    881.179816   7470.904615  -1306.382532  12152.570432  -4557.952831 
##           277           278           279           280           281 
##   1809.897308   -291.797112   7947.321926  -5021.768032  -2631.797234 
##           282           283           284           285           286 
## -11125.838558  -2423.635750  18919.544244   7869.624173   2760.712035 
##           287           288           289           290           291 
##   -608.406524    951.483337   6451.962249   6892.749723 -18804.813292 
##           292           293           294           295           296 
## -10968.436609  -7841.114663  10014.474385   3315.365140   -968.615734 
##           297           298           299           300           301 
##  27623.195970  10024.719072   4792.486825   9399.088468   2684.832854 
##           302           303           304           305           306 
##  -1187.250272   7793.796436 -24436.840263  -3399.831179      4.628188 
##           307           308           309           310           311 
##  -6780.963767  -3712.158281   3227.252254  -8932.799254  -2883.803330 
##           312           313           314           315           316 
##  -7820.477718   1997.046683  -2757.194970   2454.504697  -3717.206501 
##           317           318           319           320           321 
##  27834.129894   -645.310769   3390.665924  10908.826450   5574.748890 
##           322           323           324           325           326 
##  32336.592044   4778.540105 -21253.164823   1748.468032   1080.534109 
##           327           328           329           330           331 
##  -6475.797054  -1652.548787 -33151.155925   1380.356218  -1835.799622 
##           332           333           334           335           336 
##    376.604568  -2717.868813   4548.684747    -36.680911  -6561.830258 
##           337           338           339           340           341 
##  -2667.778363  -1730.433942  -7216.762255   4371.864085   -919.348912 
##           342           343           344           345           346 
##  -1292.610584   -550.713547    610.367423    894.772573  -1227.198376 
##           347           348           349           350           351 
##  -9053.290789 -12734.597964   2897.107296  -3797.461025  -3117.941730 
##           352           353           354           355           356 
##  -5433.033981   2327.203942   1905.717373   3228.793126  -3346.426818 
##           357           358           359           360           361 
##    -77.103757   1099.746711   7408.572354    579.869920    255.393689 
##           362           363           364           365           366 
##   2871.703910  -2493.552626   -591.138507  -8450.324820  -4241.118178 
##           367           368           369           370           371 
##  -5790.154735  -4477.019608  -6749.411191   5569.642976    836.902039 
##           372           373           374           375           376 
##   7557.229464  -7297.297404  -1849.807930  -2966.637488  -2026.185821 
##           377           378           379           380           381 
## -12009.218554   2465.578295 -10126.328270   6291.066379   9829.697476 
##           382           383           384           385           386 
##   3489.076250  -2083.694139   1937.827292   7049.531848  11634.296235 
##           387           388           389           390           391 
##  -5704.423751  -5189.729649     79.213378   8800.709405   1956.597719 
##           392           393           394           395           396 
##  11352.275184  -9862.633168   2919.431868    835.451138    688.281697 
##           397           398           399           400           401 
##   -523.267590   -414.822049 -14323.985280   8860.278260   -943.784448 
##           402           403           404           405           406 
##  -1119.003518   7251.694049  -7742.017422  -1008.666276  -2229.441643 
##           407           408           409           410           411 
##  -5490.237640  -2468.712047  -3505.749479  -8313.751388   6655.689314 
##           412           413           414           415           416 
##   2064.875201  -6985.749371  -7234.887630  14744.024657   4145.284570 
##           417           418           419           420           421 
##   4765.408108  -7818.967281  -4432.033822  -2239.274131   3201.908671 
##           422           423           424           425           426 
## -13673.133554  -2302.126514  -8601.419678   3588.571438   7483.687811 
##           427           428           429           430           431 
##   6974.081733  -3680.512086  -3773.705737  -4337.046412  -1363.637907 
##           432           433           434           435           436 
##  -5283.002273  -6151.068221  -5422.485178   -828.316367   -300.897106 
##           437           438           439           440           441 
##  -4450.559202   3132.798022   5328.542152  -4648.521246  -1709.355811 
##           442           443           444           445           446 
##   2028.905490  -3423.526884   3275.834320  -6189.478582 -11661.402748 
##           447           448           449           450           451 
##  -3946.946605  10226.933332  -1592.836417   5197.809464  -5498.001638 
##           452           453           454           455           456 
##   -696.082181    806.376153   3428.414674 -11913.830772   3850.974029 
##           457           458           459           460           461 
##  -6279.718118   7001.647885   3396.786974   2846.278406  -3540.967478 
##           462           463           464           465           466 
##   2437.517672    307.184038   2103.905843   -233.400938   3643.347059 
##           467           468           469           470           471 
##  -2384.470580   6092.952962  -6717.966415  -2654.537999  -1863.350263 
##           472           473           474           475           476 
##  -4301.684745   3403.193561   8158.541126  -5749.423154   1821.736904 
##           477           478           479           480           481 
##  -5861.961338  -2461.392378   2416.786862 -12559.259237  -9252.518837 
##           482           483           484           485           486 
##   -618.315436    583.312024   -430.999575   -828.159378  -9082.384290 
##           487           488           489           490           491 
##  11676.463819   6662.292115   7763.705479  -5180.011831   5687.167245 
##           492           493           494           495           496 
##   9551.266621   6214.777073 -13363.349549 -10287.086275  -3044.358630 
##           497 
##   -682.232315 
## 
## $fitted.values
##        2        3        4        5        6        7        8        9 
## 17209.33 20081.03 24371.65 24087.18 26460.82 23770.50 24492.99 19684.04 
##       10       11       12       13       14       15       16       17 
## 19418.33 16738.21 17522.37 14223.60 14275.20 14945.58 16656.77 14962.32 
##       18       19       20       21       22       23       24       25 
## 16005.90 15374.04 22517.47 21593.60 21068.98 22975.26 22295.44 22953.61 
##       26       27       28       29       30       31       32       33 
## 24815.46 18691.56 20432.58 28337.40 28395.55 28065.65 25674.64 27089.16 
##       34       35       36       37       38       39       40       41 
## 30965.29 31316.71 32738.02 30227.19 34177.31 37413.02 34443.78 31227.20 
##       42       43       44       45       46       47       48       49 
## 30065.05 20563.46 28146.94 30604.40 31702.98 38599.97 38090.20 42792.53 
##       50       51       52       53       54       55       56       57 
## 47065.28 39700.31 34222.00 29201.84 22287.24 28631.41 25182.60 21448.32 
##       58       59       60       61       62       63       64       65 
## 25894.64 27165.03 27464.60 27879.97 23715.35 40449.48 42309.32 37514.35 
##       66       67       68       69       70       71       72       73 
## 41757.51 46715.49 57475.51 55472.18 40588.32 38072.70 41116.59 35367.66 
##       74       75       76       77       78       79       80       81 
## 30780.74 21404.14 24633.18 20523.82 22627.84 17479.43 19512.21 18732.07 
##       82       83       84       85       86       87       88       89 
## 17751.43 15803.73 17092.50 20731.62 25187.38 26185.77 26210.95 26832.99 
##       90       91       92       93       94       95       96       97 
## 30993.59 29814.07 30822.03 28869.80 28062.73 28434.04 28844.12 22366.28 
##       98       99      100      101      102      103      104      105 
## 25407.28 18377.75 17224.04 15247.90 15550.23 16233.45 20744.59 19820.21 
##      106      107      108      109      110      111      112      113 
## 23361.53 23149.69 24840.24 27741.76 25218.35 21631.21 21909.00 24578.05 
##      114      115      116      117      118      119      120      121 
## 35536.07 33713.36 35566.65 38590.78 40563.52 38232.05 33008.50 29318.40 
##      122      123      124      125      126      127      128      129 
## 31419.15 29684.30 30876.03 38541.19 38180.57 37234.04 34070.58 35867.10 
##      130      131      132      133      134      135      136      137 
## 41311.43 40746.91 31872.91 33148.59 36365.80 32745.56 31118.95 30196.08 
##      138      139      140      141      142      143      144      145 
## 26723.49 28150.08 27917.73 25584.12 27625.97 26238.89 19785.97 22842.57 
##      146      147      148      149      150      151      152      153 
## 20650.57 23653.29 24197.93 25802.00 25985.64 27654.03 28965.97 32024.20 
##      154      155      156      157      158      159      160      161 
## 27455.41 26712.03 24246.26 30191.05 41110.94 39426.82 36769.09 41832.55 
##      162      163      164      165      166      167      168      169 
## 43253.23 46698.51 42125.44 37392.27 42864.17 59306.54 61537.69 59938.15 
##      170      171      172      173      174      175      176      177 
## 56736.55 55122.03 57847.85 56863.55 49089.39 51937.17 55711.98 55748.48 
##      178      179      180      181      182      183      184      185 
## 62938.61 53359.24 50082.24 40811.88 32278.47 35816.90 46007.24 45458.58 
##      186      187      188      189      190      191      192      193 
## 51462.52 57256.89 68132.59 73460.87 67101.35 67296.45 74427.54 70066.82 
##      194      195      196      197      198      199      200      201 
## 65551.15 54702.51 48681.32 50117.86 45674.69 37776.17 44256.15 42468.31 
##      202      203      204      205      206      207      208      209 
## 42103.17 42585.62 49438.05 58403.68 58030.11 59769.74 61439.77 65263.54 
##      210      211      212      213      214      215      216      217 
## 74811.44 66826.20 54913.72 49476.18 40395.12 37326.69 40467.12 30401.52 
##      218      219      220      221      222      223      224      225 
## 47520.94 54904.66 55814.31 78775.43 86334.86 88355.31 96011.91 86885.04 
##      226      227      228      229      230      231      232      233 
## 80831.88 80410.22 77031.39 76185.68 80963.41 82339.41 76727.04 71898.84 
##      234      235      236      237      238      239      240      241 
## 77601.08 64134.12 56102.17 47983.63 39523.11 43751.04 45922.28 39316.68 
##      242      243      244      245      246      247      248      249 
## 32941.93 43312.76 37510.21 41479.74 33677.13 32371.87 36058.57 38906.73 
##      250      251      252      253      254      255      256      257 
## 29657.38 35646.47 39480.56 44719.98 47461.65 46949.87 57339.34 74986.91 
##      258      259      260      261      262      263      264      265 
## 74800.37 68053.55 69548.11 65736.43 67194.99 60899.03 50081.49 46074.71 
##      266      267      268      269      270      271      272      273 
## 46285.91 42359.18 51238.98 47480.03 51662.40 49762.79 53865.11 54163.67 
##      274      275      276      277      278      279      280      281 
## 60232.81 57846.72 67602.81 61475.39 61687.23 60022.11 65814.34 59490.94 
##      282      283      284      285      286      287      288      289 
## 56025.27 45487.78 43870.74 61251.09 66828.72 67241.69 64637.09 63716.61 
##      290      291      292      293      294      295      296      297 
## 67751.96 71695.81 52529.01 42545.97 36505.53 46915.63 50185.33 49291.66 
##      298      299      300      301      302      303      304      305 
## 73696.00 79692.51 80365.91 85018.02 83201.11 78188.63 81685.27 56368.26 
##      306      307      308      309      310      311      312      313 
## 52597.23 52274.25 46011.02 43196.46 46830.80 39318.95 38030.05 32544.81 
##      314      315      316      317      318      319      320      321 
## 36361.91 35536.21 39400.64 37367.73 63375.88 61198.48 62836.03 70902.97 
##      322      323      324      325      326      327      328      329 
## 73310.84 99011.75 97375.45 72997.67 71785.18 70128.37 62010.83 59108.30 
##      330      331      332      333      334      335      336      337 
## 28798.07 32517.37 32960.68 35300.58 34635.74 40452.40 41537.26 36743.92 
##      338      339      340      341      342      343      344      345 
## 35951.58 36079.33 31357.99 37408.63 38077.75 38338.43 39221.78 41023.08 
##      346      347      348      349      350      351      352      353 
## 42860.77 42610.29 35494.17 25980.75 31371.46 30222.66 29809.18 27405.08 
##      354      355      356      357      358      359      360      361 
## 32124.28 35910.92 40413.00 38586.39 39857.54 42014.43 49473.42 50028.75 
##      362      363      364      365      366      367      368      369 
## 50232.15 52716.55 50178.28 49618.04 42199.83 39372.44 35516.45 33275.98 
##      370      371      372      373      374      375      376      377 
## 29299.79 36650.53 38957.20 46910.73 40830.38 40272.78 38797.47 38326.22 
##      378      379      380      381      382      383      384      385 
## 29115.14 33752.90 26744.65 35034.87 45457.07 49053.27 47311.74 49320.61 
##      386      387      388      389      390      391      392      393 
## 55594.42 65161.71 58314.44 52734.93 52461.29 59904.55 60432.44 69175.92 
##      394      395      396      397      398      399      400      401 
## 58187.57 59767.98 59324.29 58803.70 57277.54 56028.41 42672.72 51332.50 
##      402      403      404      405      406      407      408      409 
## 50324.29 49281.59 55738.16 48216.24 47521.44 45833.67 41473.57 40294.18 
##      410      411      412      413      414      415      416      417 
## 38341.32 32384.45 40325.27 43276.89 37903.17 32948.98 47949.14 51827.16 
##      418      419      420      421      422      423      424      425 
## 55790.40 48194.46 44485.99 43150.52 46767.99 35086.98 34813.85 29023.00 
##      426      427      428      429      430      431      432      433 
## 34661.17 43060.78 50012.51 46749.99 43793.33 40691.92 40579.15 37026.50 
##      434      435      436      437      438      439      440      441 
## 33131.49 30341.60 31931.33 33796.70 31784.06 36692.31 42951.52 39675.78 
##      442      443      444      445      446      447      448      449 
## 39379.24 42411.67 40279.45 44303.48 39509.26 30463.95 29291.35 40746.55 
##      450      451      452      453      454      455      456      457 
## 40425.33 46125.43 41723.80 42076.48 43711.01 47461.40 37248.03 42139.29 
##      458      459      460      461      462      463      464      465 
## 37522.92 45157.50 48708.01 51351.25 48052.48 50413.53 50616.81 52378.97 
##      466      467      468      469      470      471      472      473 
## 51872.22 54841.47 52146.62 57241.54 50443.11 48033.35 46607.26 43202.38 
##      474      475      476      477      478      479      480      481 
## 46991.03 54518.99 48897.69 50615.68 45359.39 43724.36 46581.83 35904.38 
##      482      483      484      485      486      487      488      489 
## 29410.17 31295.69 34015.71 35518.59 36492.81 30078.54 42717.28 49435.15 
##      490      491      492      493      494      495      496      497 
## 56324.58 50990.26 55865.16 63564.94 67409.35 53546.66 44042.93 42050.80 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8529
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##       original     bias    std. error
## t1*    4.78069  0.5572868     2.89241
## t2* 1693.33106 27.1758158   241.48170
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    1.421938       4.960141   10.65103
## 2    lag_depvar 1350.264574    1704.691123 2145.96890

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Sep 26 01:03:49 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Sep 26 01:03:58 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Sep 26 01:04:07 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Sep 26 01:04:16 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Sep 26 01:04:25 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Sep 26 01:04:34 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Sep 26 01:04:43 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Sep 26 01:04:52 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Sep 26 01:05:02 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Sep 26 01:05:11 2022
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021|2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_23 %>% 
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))
Item 2023 2022 2021 2020
Agua NA 5.496500 5.70810 7.294219
Comida NA 285.084375 304.77170 337.832438
Comunicaciones NA 0.000000 0.00000 0.000000
Electricidad NA 48.842750 37.25090 31.008531
Enceres NA 13.849375 14.42045 23.642281
Farmacia NA 2.747500 9.49665 11.199188
Gas/Bencina NA 56.943750 30.92770 25.801687
Diosi NA 16.628375 38.26405 37.835531
donaciones/regalos NA 0.000000 8.60410 8.584969
Electrodomésticos/ Mantención casa NA 5.916000 36.32340 25.920875
VTR NA 27.240000 22.34815 21.135250
Netflix NA 7.607125 7.26010 7.630281
Otros NA 4.726625 1.89065 1.181656
Total 0 475.082375 517.26595 539.066906
## Joining, by = "word"


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")

tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: 35 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:1743, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2022-10-09 00:04:58 sería de: 36.024 pesos// Percentil 95% más alto proyectado: 39.527,33

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 34620.70 34573.97
Lo.80 34807.56 34841.43
Point.Forecast 36024.04 38454.70
Hi.80 37925.43 43096.21
Hi.95 38971.37 45553.27


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1      mean
##       0.3573  986.4061
## s.e.  0.1481   38.4438
## 
## sigma^2 = 27945:  log likelihood = -280.18
## AIC=566.35   AICc=566.97   BIC=571.63
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1     xreg
##       0.4100  33.1028
## s.e.  0.1482   1.4427
## 
## sigma^2 = 29234:  log likelihood = -281.17
## AIC=568.34   AICc=568.95   BIC=573.62
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 922.8188 635.6049 664.3815
Lo.80 1049.9955 757.0292 743.5117
Point.Forecast 1290.2380 986.4050 919.6053
Hi.80 1530.4805 1215.7809 1216.7059
Hi.95 1657.6572 1337.2051 1411.0561


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 42 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Andrés, Tami
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Andrés Tami
1 marzo_2019 68268 175533
2 abril_2019 55031 152640
3 mayo_2019 192219 152985
4 junio_2019 84961 291067
5 julio_2019 205893 241389


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.2.7  bsts_0.9.8          BoomSpikeSlab_1.2.5
##  [4] Boom_0.9.10         MASS_7.3-54         scales_1.2.1       
##  [7] ggiraph_0.8.3       tidytext_0.3.4      DT_0.25            
## [10] autoplotly_0.1.4    rvest_1.0.3         plotly_4.10.0      
## [13] xts_0.12.1          forecast_8.17.0     wordcloud_2.6      
## [16] RColorBrewer_1.1-3  SnowballC_0.7.0     tm_0.7-8           
## [19] NLP_0.2-1           tsibble_1.1.2       forcats_0.5.2      
## [22] dplyr_1.0.10        purrr_0.3.4         tidyr_1.2.1        
## [25] tibble_3.1.8        ggplot2_3.3.6       tidyverse_1.3.2    
## [28] sjPlot_2.8.11       lattice_0.20-45     gridExtra_2.3      
## [31] plotrix_3.8-2       sparklyr_1.7.8      httr_1.4.4         
## [34] readxl_1.4.1        zoo_1.8-11          stringr_1.4.1      
## [37] stringi_1.7.8       DataExplorer_0.8.2  data.table_1.14.2  
## [40] reshape2_1.4.4      fUnitRoots_4021.80  plyr_1.8.7         
## [43] readr_2.1.2        
## 
## loaded via a namespace (and not attached):
##   [1] utf8_1.2.2          tidyselect_1.1.2    lme4_1.1-30        
##   [4] htmlwidgets_1.5.4   munsell_0.5.0       codetools_0.2-18   
##   [7] effectsize_0.7.0.5  its.analysis_1.6.0  withr_2.5.0        
##  [10] colorspace_2.0-3    ggfortify_0.4.14    highr_0.9          
##  [13] knitr_1.40          uuid_1.1-0          rstudioapi_0.14    
##  [16] TTR_0.24.3          labeling_0.4.2      emmeans_1.8.1-1    
##  [19] slam_0.1-50         bit64_4.0.5         farver_2.1.1       
##  [22] datawizard_0.6.1    fBasics_4021.92     rprojroot_2.0.3    
##  [25] vctrs_0.4.1         generics_0.1.3      xfun_0.33          
##  [28] R6_2.5.1            bitops_1.0-7        cachem_1.0.6       
##  [31] assertthat_0.2.1    networkD3_0.4       vroom_1.5.7        
##  [34] nnet_7.3-16         googlesheets4_1.0.1 gtable_0.3.1       
##  [37] spatial_7.3-14      timeDate_4021.104   rlang_1.0.6        
##  [40] forge_0.2.0         systemfonts_1.0.4   splines_4.1.2      
##  [43] lazyeval_0.2.2      gargle_1.2.1        selectr_0.4-2      
##  [46] broom_1.0.1         yaml_2.3.5          abind_1.4-5        
##  [49] modelr_0.1.9        crosstalk_1.2.0     backports_1.4.1    
##  [52] quantmod_0.4.20     tokenizers_0.2.3    tools_4.1.2        
##  [55] ellipsis_0.3.2      gplots_3.1.3        jquerylib_0.1.4    
##  [58] Rcpp_1.0.9          base64enc_0.1-3     fracdiff_1.5-1     
##  [61] haven_2.5.1         fs_1.5.2            magrittr_2.0.3     
##  [64] timeSeries_4021.104 lmtest_0.9-40       reprex_2.0.2       
##  [67] googledrive_2.0.0   mvtnorm_1.1-3       sjmisc_2.8.9       
##  [70] hms_1.1.2           evaluate_0.16       xtable_1.8-4       
##  [73] sjstats_0.18.1      ggeffects_1.1.3     compiler_4.1.2     
##  [76] KernSmooth_2.23-20  crayon_1.5.1        minqa_1.2.4        
##  [79] htmltools_0.5.3     tzdb_0.3.0          lubridate_1.8.0    
##  [82] DBI_1.1.3           sjlabelled_1.2.0    dbplyr_2.2.1       
##  [85] boot_1.3-28         Matrix_1.5-1        car_3.1-0          
##  [88] cli_3.4.1           quadprog_1.5-8      parallel_4.1.2     
##  [91] insight_0.18.4      igraph_1.3.5        pkgconfig_2.0.3    
##  [94] xml2_1.3.3          bslib_0.4.0         estimability_1.4.1 
##  [97] anytime_0.3.9       snakecase_0.11.0    janeaustenr_1.0.0  
## [100] digest_0.6.29       parameters_0.18.2   janitor_2.1.0      
## [103] rmarkdown_2.16      cellranger_1.1.0    curl_4.3.2         
## [106] gtools_3.9.3        urca_1.3-3          nloptr_2.0.3       
## [109] lifecycle_1.0.2     nlme_3.1-153        jsonlite_1.8.0     
## [112] tseries_0.10-51     carData_3.0-5       viridisLite_0.4.1  
## [115] fansi_1.0.3         pillar_1.8.1        fastmap_1.1.0      
## [118] glue_1.6.2          bayestestR_0.13.0   bit_4.0.4          
## [121] sass_0.4.2          performance_0.9.2   r2d3_0.2.6         
## [124] caTools_1.18.2
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))